Predicting redox potentials by graph-based machine learning methods.
Linlin JiaÉric BrémondLarissa ZaidaBenoit GaüzèreVincent TognettiLaurent JoubertPublished in: Journal of computational chemistry (2024)
The evaluation of oxidation and reduction potentials is a pivotal task in various chemical fields. However, their accurate prediction by theoretical computations, which is a complementary task and sometimes the only alternative to experimental measurement, may be often resource-intensive and time-consuming. This paper addresses this challenge through the application of machine learning techniques, with a particular focus on graph-based methods (such as graph edit distances, graph kernels, and graph neural networks) that are reviewed to enlighten their deep links with theoretical chemistry. To this aim, we establish the ORedOx159 database, a comprehensive, homogeneous (with reference values stemming from density functional theory calculations), and reliable resource containing 318 one-electron reduction and oxidation reactions and featuring 159 large organic compounds. Subsequently, we provide an instructive overview of the good practice in machine learning and of commonly utilized machine learning models. We then assess their predictive performances on the ORedOx159 dataset through extensive analyses. Our simulations using descriptors that are computed in an almost instantaneous way result in a notable improvement in prediction accuracy, with mean absolute error (MAE) values equal to 5.6 kcal mol - 1 for reduction and 7.2 kcal mol - 1 for oxidation potentials, which paves a way toward efficient in silico design of new electrochemical systems.
Keyphrases
- machine learning
- neural network
- density functional theory
- molecular dynamics
- convolutional neural network
- artificial intelligence
- electron transfer
- big data
- deep learning
- hydrogen peroxide
- healthcare
- gold nanoparticles
- magnetic resonance imaging
- molecular dynamics simulations
- monte carlo
- molecular docking
- quality improvement
- nitric oxide
- visible light
- computed tomography
- drug discovery